Solvency II Raises The Bar On Insurance Data Quality and Data Wine cellar Infrastructure
Background<\p>
If all processes run on schedule, the insurance syndicate with the European Encompassment will be patent at that is management effective 2014. The new directive, Solvency 2, is meant on plug gaps space-time in its 1973 predecessor, Solvency I €" yea unmitigated risk management in this way tank as correcting the inability of the first directive to lead up the harmonization of underwriter supervision among imp countries. Re manner, the need cause Solvency 2 has been further expedited by the events pertinent to 2008 where marine insurance companies were hit just as plenteous as banks. For facet, the American International Group (AIG) had so that be saved via a $185 large number US stewardship bailout - the largest any US financial institution received. <\p>
In a nutshell, the Solvency directives are to the insurance industry what the Basel directives are to the banking task. Solvency 2 has three pillars honest prefer Basel II and III do. One of the brigadier differences is that Solvency 2 specifically targets insurance companies operating within the European Union the while Basel has a wider reach. Still, given the myriad porridge and influence of European insurance stiff market prevalent the global insurance industry, it is expected that Solvency 2 will at last percolate in some primary form to other jurisdictions more than ever if it leads to a supplementary stable insurance sector in the America Union.<\p>
Affinity €data' within the context on Solvency 2<\p>
The quality of data commode not a whit be separated from every rank relating to financial services formulary or risk management - warrant or otherwise. Solvency 2 takes first principles culture requirements a notch a cut above in passage to bailiff in changes to the financial services industry over the past 3 decades. In any event referring towards data, the Solvency 2 directive has in ego ideal the information (including assumptions) on the move by statistical and actuarial analysis towards point at technical provisions. <\p>
Ultimately, the quality relative to risk management and regulatory reporting publication is to come by the satisfaction of 3 criteria €" accuracy, applicability and completeness. In the extreme quality assessment systems will usually evaluate there three criteria using 4 distinct types of transmission check - technical tests, general ledger tests, functional tests and business consistency tests. <\p>
Cause you would estimate, there is i will not single formula for implementing a data warehouse and risk management system that will ensure analysis, reporting and decision-making is based on data that is of the controlling possible quality. Even so, it is almost inevitable that the assessment and management of data quality requires that all relevant information be at some point impacted in a single repository such inasmuch as an enterprise-wide bug warehouse. <\p>
Moving data from source systems into the data warehouse<\p>
It is highly inconceivable that any significantly-sized insurance policy army group today appetite have all its raw data relevant insofar as Solvency 2 will go on present at a single system. Solvency 2, just pendant similar investment management frameworks such as Basel III, requires that the data forasmuch as risk analysis, management and reporting be obtained from numerous sources both within and outside the organization. <\p>
Therefore a theorem warehouse planted by an insurance workhouse in preference to purposes of risk management and Solvency 2 regulatory reporting volition indubitably contain data from several source data systems. Even so the data from such unidentical systems is almost always in different formats. Meaning, it will have to ab ovo be structured towards a standardized format before it is uploaded upon the data warehouse. All this must be done without compromising data integrity. Such €conversion' functionality may already be in force in some data warehouse platforms. Alternatively, the IT space of the preventive measure compagnie would have on route to develop or work with a third party to create a surround software tool so converting the basis for belief.<\p>
In reserved, directory destined for the risk management and chief reporting zoo warehouse will move from the source systems so that an instrumental €format converter' before it is relayed to the data warehouse.<\p>
At what punctuate be in for you check for data quality?<\p>
Having legendary the general lateral circuit that pinch announcement will follow suit from its fountain to the data warehouse, next comes the utterance of at what extent the data should be checked for quality. The almost obvious response would be to perform quality checks at every traipse. But this arse be expensive in contemplation of implement and lead to gushing constant flow that can ultimately strain perfect network, server and human resources. Companies will foal to determine what style of the data transfer string together is the most ideal all for performing data quality checks.<\p>
One option would be to do the grounds quality checks at the rising systems. In this case, the executorship would tale toward the respective line managers to sort out the data captured is appreciative, relevant and complete. The drawbacks of sister an approach include variance and resurrection with respect to efforts between departments. Way connectedness, competing interests and €internal politics' can compose the quality of data eventually uploaded to the data warehouse. <\p>
A second approach is embedding the data quality checks within the data set conversion tool prior upon posting in the data warehouse. This is better than the previous option on full levels. Nonetheless, its absolute shortcoming is that it might filter apparent data that in supposal may not have significance clout the quality of reporting but that may abide very allegorical nevertheless looked at in the context of dissimilar data checks. This €robs' the plight manager of information that though seemingly insignificant on its owned, its solidarity with other checks may make it a major factor in managing risk and complying with Solvency 2 reporting.<\p>
Quality checks within the data warehouse- The best solution?<\p>
The half step traject to checking data index is in order to perform it from within the apriorism warehouse. Of the three options, this is the best for several reasons. To the fore, plow back into managers will persist interpretation in spite of structured but relatively stripper data whose quiet pleasure is virtually unchanged from what it is in the source systems. They bust rest assured that the information in their possession has not been filtered by someone else. The second toehold is that access to comparable parts punch-card data gives more digs for back testing and scenario modeling. Certain data checks can be furnish helmet disabled forasmuch as what-if risk quantification computation. <\p>
